We considered  the Cauchy problem of a class of quasilinear parabolic equations with strong absorption terms. Due to the effect of the strong absorption term, the solution to the problem could  possess compact support and extinguish  at a finite time. Firstly, by using  the comparison principle and constructing suitable supersolutions, it was proven that the solution possessed a uniform compact support after a certain time and even after any positive time. Secondly, under some conditions, it was proven that the solution extinguished at a finite time by using the L¹ norm estimates of the solution to the problem at different times.

We considered  the decay rate of solutions to a class of nonlocal wave equations with  Kirchhoff term. Firstly, by establishing weighted estimates of the Sobolev norm, the difficulty of classical multiplier method failure caused by  supercritical damping term could be overcome. Secondly, by using  the weighted multiplier method to prove that the energy functional of the studied  problem decayed  logarithmically when  the damping term was supercritical damping,  which was  totally different from both exponential decay of linear damping and polynomial decay of subcritical damping.

Let T be a quasinilpotent operator on an infinite dimensional complex Banach space X and x∈X＼{0}. Let Λ(T)={k_x: x≠0}, and call it the power set of T. We prove that Λ(T) is right closed, that is, sup σ∈Λ(T) for each nonempty bounded subset σ of Λ(T). In particular, we prove that for any infinite dimensional complex Banach space X, there exists a quasinilpotent operator T on X such that Λ(T)=［0,1］.

We considered an  independent replicatedly observed model of  INAR(1) (PLINAR(1)) process with Poisson-Lindley marginal distribution for overdispersed replicatedly observed time series data. Firstly, by using conditional least squares estimation, Yule-Walker estimation, quasi-likelihood estimation, and conditional maximum likelihood estimation methods to estimate the parameters of the model, we discussed the asymptotic properties of the estimators and gave predictions for the model. Secondly, through numerical simulations, the performance of different estimation methods and the impact of replicated observations were compared. Finally, a data set of the number of sunspot groups per week from replicated observations was fitted to this model, the fitting results validated the effectiveness of the model.

We proposed a blind image deblurring and super-resolution reconstruction algorithm based on partial differential equations (PDE). The goal was to reconstruct clear, high-resolution images from noisy, low-resolution blurred images without prior knowledge of the blur kernel. Firstly, we constructed a variational problem for the image degradation process and derived a PDE model by using variational methods. Secondly, by combining the alternating direction method and numerical difference method, we designed a spatiotemporal fully discrete numerical scheme to solve the unknown blur kernel and the clear image. Thirdly, through a series of numerical experiments, we analyzed the impact of parameter selection on image reconstruction performance and  determined appropriate parameter settings. Finally, experiments were conducted on several remote sensing images, and the experimental results proved  the effectiveness and reliability of the proposed model.

Aiming at  the problem that the traditional EGARCH model was difficult to capture long-term memory, we proposed an  fBm-EGARCH model by introducing fractional Brownian motion. We gave the second-order moment, the fourth-order moment and covariance function properties of the model, and   theoretically  proved its long-term memory. Numerical simulation results show that the model can not only accurately capture short-term fluctuations, but also reflect long-term memory, which verifies the effectiveness of the model.

A complex symmetric  operator refers to a linear operator with a symmetric matrix representation  on a  Hilbert space. We review the main research  advances  and  several open problems of complex symmetric operators in recent years, involving special complex symmetric operators, reducing subspaces, the norm closure problem, and algebraic properties  and so on.

In order to accurately and quickly verify the integrity of multiple entity spatial topological relationships in geographic linked data, 
we proposed a saturation reasoning method based on the RCC8 model to detect whether multiple spatial topological relationships had conflicts. The method applied reasoning rules to perform pairwise combination reasoning of relationship pairs, gradually inferring potential spatial relationships and verifying the reasoning results. The above process was repeated continuously until it was ensured that the reasoning process terminated after all relationship pairs had been fully derived. On this basis, we encapsulated  a saturation reasoning verification component based on RCC8 model to facilitate  users in verifying  multiple spatial topological relationships without the need for repetitive coding by directly invoking the component. By using the method and component to test  partial data from the linked dataset of the SLIPO project, multiple  spatial topological conflict entities that the GeoSHACL component could not detect were successfully identified.

Aiming at  the problem of poor performance exhibited by current deep learning  in extreme weather phenomenon Madden-Julian oscillation (MJO) prediction tasks,  we proposed a time series prediction model based on a combination of dynamic graph neural network and Transformer. Firstly, we mapped the two-dimensional grid of Earth’s land and sea to the nodes of graph structure, 
and proposed  a method of using  multi-attention hybrid sea and land masks for node screening. Secondly, we iteratively updated edge weights based on heat conduction and node similarity measurement to obtain the most accurate climate model information at each time step. Thirdly, we used the maximum extreme value method to extract abnormal node information during different time periods as occurrence points of extreme climate, and strengthened variable weights of these points. Finally, we input above results into a graph neural network for encoding and utilized Transformer for decoding operations  to obtain prediction results. 
Experimental results show that the model can achieve an effective bivariate correlation coefficient (COR) prediction value of up to 39 d as well as an effective root mean square error (RMSE) prediction value of 31 d in prediction, and its performance is superior to existing models.

Based on a path reasoning method of graph encoder, we used the entity relationships between multi rounds of dialogue in the knowledge graph as a node graph. The encoder sequentially encoded the nodes according to each round of dialogue to simulate the semantic reasoning process, and utimately predicted the answer entity for the current dialogue. This approach solved the problems of missing words and pronouns in dialogues, as well as feature extraction problems in complex contexts. The experimental results show that the method focused more on the relationships between entities, which helped to maintain the integrity and accuracy of reasoning. To a certain extent, it proved the practicality and effectiveness of modeling context as a relational node graph.

Aiming at the problem that in the implementation process of safety reinforcement learning, the implementation approach based on shielding might  be constrained by the lack of suitable alternative policies available, which resulted in the inability to prevent  the system from leaving a safe state even if danger was detected. Although the implementation approach of  knowledge integration could  provide safety guidance for specific states by extracting conceptual features and applying structured knowledge, sometimes the guidance embedded in knowledge might not be the optimal strategy, and might even be inferior to  the strategies learned by agent exploration. We proposed an optimization strategy for safety reinforcement learning guided by ontology to achieve  risk 
identification avoidance and  action generation optimization. Based on this theory, we designed and implemented a simulation system in the scenario of unmanned aerial vehicle  obstacle avoidance, and verified  the effectiveness by using  five different reinforcement learning algorithms. The experimental results show that the optimization strategy for safety reinforcement learning based on  ontology guidance can achieve  alternative policy selection for intelligent agents on the basis of shielding risky actions, and has better performance than  traditional reinforcement learning methods.

Aiming at the problems of high input dimensionality and long training time in traditional game intelligent  algorithm models, we  proposed a novel deep reinforcement learning game intelligent  guidance algorithm that integrated state information transformation and reward function shaping techniques. Firstly, using  the interface provided by the Unity engine to directly read game backend  information effectively compressed  the dimensionality of the state space and reduced the amount of input data. Secondly, by finely designing  the reward mechanism, the convergence process of the model was accelerated. Finally, we conducted comparative experiments between the proposed algorithm model and existing methods  from both subjective qualitative and objective quantitative perspectives. The experimental results show that this algorithm not only significantly improves the training efficiency of the model,  but also markedly enhances the performance of the  agent.

We proposed a  method for automatically identifying components with missing readable text in Android applications to improve the accessibility of these applications. Firstly, We used UI Automator to extract the graphical user interface of the application,  prune  irrelevant components and  complete  component attributes to generate the corresponding view tree. Secondly, we designed three heuristic rules  to identify components with missing readable text in the view tree. Through evaluation experiments on six popular applications, the proposed method successfully identified problematic components with an average accuracy  of 97%. Finally, a generated test report helped application developers  clearly locate and rectify the missing readable text by marking the problematic components in both the source code and screenshots. The  research achievement not only effectively improves the user experience for visually impaired users, enabling them to  interact with the applications more smoothly, but also provides developers with a practical tool to promote the overall accessibility of Android applications. Through  this approach, developers can better understand and address accessibility issues, creating a more user-friendly digital environment for all users.

Aiming at the problems of   less obvious or fewer sentiment words and euphemistic expressions in current implicit sentiment statements, we proposed an implicit sentiment analysis method based on semantic feature extraction. The method  introduced factual information related to implicit sentiment statements as auxiliary features, and used RoBERTa pre-training model to perform deep semantic interaction between the text and its auxiliary features in order to obtain global features. At the same time, a bidirectional gated recurrent unit (BiGRU) was used to capture local features, and finally, the sentiment weight was calculated by combining with attention pooling technique, so as to identify and understand the implicit sentiment information more accurately. The simulation experiments were conducted on  Snopes and PolitiFact datasets, and the results show  that the method has excellent performance  in implicit sentiment analysis. It not only surpasses existing methods in multiple evaluation metrics, but also significantly improves the overall performance, providing an effective solution for a wider range of sentiment analysis application scenarios, especially when dealing with complex and indirectly expressed sentiment content, it has important application value and significance.

We consider a variant of the classic problem of  the traveling salesman problem (TSP) in combinatorial optimization problem: 
 the close enough traveling salesman problem (CETSP).  Firstly, we comprehensively introduce the history, solving methods, and algorithms for both TSP and CETSP, including exact algorithms (such as branch and bound method, linear programming) and heuristic algorithms (such as particle swarm optimization, greedy algorithms, etc.). The TSP requires finding the shortest path to visit each city  once and return to the starting point given a list of cities and distances. CETSP is a generalization of TSP, allowing the visiting point for each target to be chosen from within a specified neighborhood, rather than  exact location. It is  suitable for practical applications that can  tolerate errors, such as logistics distribution, intelligent transportation, and wireless sensor networks, etc. CETSP has higher flexibility and adaptability, which can significantly reduce computational resources and time consumption, particularly for large-scale problems with greater advantages. Secondly, we introduce  the potential  of CETSP in practical applications, especially in logistics, industrial manufacturing, traffic planning, information and communication, offering effective solutions for improving efficiency, reducing costs, and promoting intelligent decision-making. Finally, we have identified some future research directions for CETSP.

The purpose of mobile Internet information accessibility (MIIA) was to ensure that mobile application content was equally accessible, convenient, and barrier-free for all users, including those with visual impairments.  We systematically review the latest research progress in the field of mobile Internet information accessibility, focusing on the analysis and summary of research achievements in  semantic representation and understanding of mobile GUI, accessibility detection and layout repair. The analysis shows that from traditional heuristic rule methods to deep learning-driven automated tools, related technologies have gradually improved detection accuracy and adaptability, while also revealing challenges in addressing complex dynamic interactions and diverse user needs. We have provided an outlook on  future research directions.  MIIA technologies have significantly improved  the digital experience for visually impaired users, but they still need continuous innovation and optimization  to achieve a truly inclusive digital society.

We review  the  floorplanning methods for very large scale integration (VLSI), explore the significance of floorplanning in integrat
ed circuit design, and its impact on chip area, interconnect length, and design cycle. Firstly, we  review the development history of integrated circuit technology, emphasize the role of floorplanning in determining the position, size, and rotation angles of modules. Secondly, we provide a detailed introduction to four main categories of VLSI floorplanning methods: intuitive construction methods, analytical methods, iterative methods and machine learning methods. Thirdly, we discuss two commonly used  MCNC and GSRC benchmark datasets, which are crucial for testing and evaluating floorplanning methods in the VLSI design field. Finally, we summarize the research progress in the field of floorplanning and point out future research directions.

We synthesized manganese-based perovskite La_1-x-yCa_xK_yMnO₃ (LCKMO) by hydrothermal method and studied the effect of anion mole fraction on the Mn—O bond  length duing its  hydrothermal growth process, as well as its regulatory effect on magnetic properties. The research results show  that all samples have a perovskite structure and exhibit superlattice reflection under  K⁺  doping. The surface of  the prepared samples presents smooth single-crystal cubic morphology. The Mn─O bond length in perovskite is positively correlated with the anion mole fraction during the reaction growth process. The LCKMO samples with different Mn—O bond lengths all  exhibit room\|temperature ferromagnetism,  but there are   significant differences  in their saturation magnetization and coercivity at the same temperature. The blocking temperature of the samples  increases with the increase of Mn—O bond length. The research results  provide experimental guidance for optimizing the magnetic properties of perovskite materials and deepen the understanding of the underlying physical processes.

With the rapid depletion of fossil fuels and increasing pollution,  the development and utilization  of clean energy are becoming increasingly important. Photocatalytic technology that  converts solar energy into clean hydrogen energy  is  an effective solution. It is necessary to solve the contradiction between  the bandgap of photocatalysts and the intensity of sunlight  due to limitations in water splitting electrode potential. Therefore,  it is highly significant to develop and utilize photocatalysts with visible light  response capability. We review  the development and principles of photocatalysts,  discuss their immense potential for advancement, and introduce the most  common photocatalysts and  current research progress.

Cellulose materials as a novel class of adsorbents,  it has excellent mechanical properties and chemical stability,   their adsorption performance can be significantly enhanced through chemical modification. In the context of removing pollutants from aqueous solutions,  cellulose materials show good adsorption capabilities for contaminants such as heavy metal ions,  organic small molecules,  and microplastics,   they can also be used as  blood purification adsorbent for the treatment of blood-related diseases. We review the application of cellulose materials in the purification of aqueous solutions,   and  summarize the research process of cellulose materials as adsorbents.

Exploring the dynamic detail characteristics of protein folding,  assembly,  and phase separation at the molecular level is currently the focus and difficulty of research in this field, and  coarse-grained model have become a key strategy to address these issues. We review the development history of coarse-grained model of protein, introduce two commonly used coarse-grained  models, and explain their modeling methods,  potential energy functions,  and applications in practical biological systems. By demonstrating the application advantages of these models in simulating complex protein systems,  we review  the unique value of coarse-grained model in significantly reducing computational resource consumption, as well as their  potential and significance in advancing the study of large-scale protein dynamic processes.

 We used network pharmacology and molecular docking technology to study the  mechanism of Wutou decoction (WTD) combined  with three traditional Chinese medicines in combating  rheumatoid arthritis (RA), and validated the pathways and targets through cell experiment. The  results show that the  PI3K-Akt signaling pathway and NF-κB signaling pathway are key pathways. The targets  such as AKT1,EGFR,HIF1A and NFKB1 play important roles in the anti RA effect of WTD. The  research results revealed the  key pathways for combating RA and elucidated the relevant  mechanisms of compatibility between WTD and three traditional Chinese medicines,  providing a new direction for the study of traditional Chinese herbal medicine compatibility mechanisms.

Based on the fact that glioblastoma multiforme is the most common malignant tumor in the brain,  an average survival time of  patients is relatively short,  and traditional treatment methods are difficult to achieve  cure. However, emerging oncolytic virotherapy has limited dissemination capability and  suboptimal treatment effects. By using genetic engineering technology to modify the capsid protein of human adenovirus type 5 (Ad5), we constructed a chimeric oncolytic adenovirus with a fiber knob replacement to enhance its ability to infect and target  glioma cells. By replacing the  knob of Ad5 with the knob  of Ad37 from subgroup B Ad37, it was confirmed that it could utilize sialic acid as a receptor, thereby improving its ability to infect and kill tumor cells with low expression of  CAR (coxsackievirus and adenovirus receptor) such as glioma, and improving its penetration efficiency through the blood\|brain barrier in in vitro models,  providing a new solution for the treatment of glioblastoma multiforme.

Mesenchymal stem cell exosomes (MSC-Exos) are a class of nanoscale vesicles with great potential in experimental research  and clinical applications. They contain a variety of biomolecules,  including miRNA,  mRNA,  proteins and lipids,  and have the function of  mediating  cell signaling and participating in regulation of receptor cells. Based on the important role of  MSC-Exos, we review the significant effects of  MSC-Exos in promoting tissue repair,  immune regulation and neuroprotection from the research progress and clinical applications,   especially in the treatment of autoimmune diseases,  neurodegenerative diseases,  cardiovascular diseases and tumors. We analyze a series of unsolved problems and application popularization challenges in its clinical application,  including elucidation of mechanism of action,  separation,  extraction and purification technology,  formulation of standardized production rules,  determination of dosage and administration route,  enhancement of stability,  and reduction of immunogenicity. This provides a basis for addressing these limitations to achieve widespread clinical application of MSC-Exos.

 Based on gene therapy as a fundamental treatment for diseases, it brings new ideas and methods for the treatment of rheumatoid arthritis (RA). We review  the relevant research advances  of gene therapy for rheumatoid arthritis,  including small interfering RNA (siRNA), micro RNA (miRNA),  DNA,  CRISPR/Cas9 system,  deoxyribonuclease and some other technologies,   providing reference ideas for the application of  gene therapy in the field of RA and offering more  effective and targeted treatment plans for the patients with RA. 

American ginseng was rich in various pharmacological active substances,  which had  various  pharmacological effects such as protecting the cardiovascular system,  improving neurological diseases,  and lowering blood sugar,  significant progress had been made in the structural characterization and processing transformation research of saponin components in American ginseng. We summarized the classification and distribution of saponin active components in American ginseng,  modern mainstream analytical techniques,  and the transformation mechanism of saponin components by processing,   providing effective technical support and methodological guidance for the in-depth exploration of saponin components in American ginseng and innovative processing techniques,  thereby expanding its applications in medical treatment,  health care  and other aspects.

SWEET (sugars will eventually be exported transporters) proteins are a novel class of sugar transporter proteins that mediate the bidirectional transmembrane transport of sugars in cells and play important functions in plant growth and development,  including phloem loading,  phytohormone transport,  flower,  fruit and seed development,  interactions between plants and pathogen, and symbiosis between plants and microorganisms.  SWEET proteins are important participant in the process of plant-pathogen interactions. We summarize the response mechanisms of SWEET proteins in biotic stresses, as well as the metabolic characteristics,  regulatory pathways and specific defense responses of SWEET genes when plants are infected with different pathogens (bacteria,  fungi,  nematodes and virus). We also discuss  the use of gene editing tools to edit SWEET genes to enhance plant resistance to pathogens and their application in agriculture. The aim is to provide a reference for in-depth research on the mechanism of  SWEET proteins involvement in plant-pathogen interactions and the use of SWEET genes for disease resistance breeding.

Sclerotinia sclerotiorum (Lib.) de Bary is a worldwide and necrotrophic phytopathogenic fungi with a wide host-range. Sclerotinia stem rot (SSR) caused in soybean and rapeseed by S.sclerotiorum has caused huge economic losses to agricultural production. The pathogenic mechanism of S.sclerotiorum is complicated,  which not only has a necrotrophic phase that directly kills cells,  but also includes a short biotrophic phase that needs to suppress plant immunity. S.sclerotiorum has a wide variety of pathogenic factors,  including key regulatory factors that mediate the formation of infection structure or stress resistance,  hydrolytic enzymes that degrade plant cell components,  oxalic acid,  effector that induce plant cell death or inhibit plant immunity,  etc. We have reviewed the infection model of S.sclerotiorum, summarized  the roles of various pathogenic factors,  especially effector proteins,  in the pathogenesis of S.sclerotiorum. Combined with the latest research,  we have prospected the new pathogenic mechanism of S.sclerotiorum,   providing theoretical basis for the prevention and control of crop Sclerotinia diaease.

Based on the    significant impact of  natural aging on the adsorption behavior of microplastics, we selected commercial polystyrene (PS) coffee cup lids as the microplastic source. Naturally aged microplastics were obtained through an outdoor natural process. Nonylphenol (NP) was chosen as the target pollutant. The adsorption kinetic and thermodynamic characteristics of NP by PS microplastics before and after natural aging were investigated. The influence mechanism of natural aging on the adsorption of NP by microplastics was studied  by combining with various characterization methods,  such as scanning electron microscopy, specific surface area analyzer, infrared spectroscopy, and X-ray photoelectron spectroscopy. The results show that the pseudo second-order (PSO) kinetic model can well fit the kinetic process of NP adsorption by PS microplastics. Both the Henry model and the Freundlich model can well fit the adsorption isotherms of NP by PS microplastics. The adsorption mechanism of NP by PS microplastics includes partitioning,  van der Waals forces,  hydrogen bonding,  π-π interactions and hydrophobic interactions. After  aging,   the π-π interactions and hydrophobic interactions between PS and NP are weakened,  thereby inhibiting the adsorption of NP. The research results provide a theoretical basis for accurately assessing the ecological risks of PS microplastics and NP.

Sediment-water interface is a key area for material exchange and energy transfer in an aquatic ecosystem, and is a necessary pathway for the exchange of substances, including pollutants and nutrients, between sediment and overlying water. The behaviors of benthic bioturbators alter the structure of the existing sediments and the balance of the material exchange near the interface, significantly affecting the local microenvironmental characteristics as well as the microbial species, numbers and community composition, and making the  environment near the interface more complex and dynamic. The bioturbation can alter the transfer, transformation and bioavailability of the pollutants, and  reconstruct the cycling pathways and fluxes of important elements such as carbon, nitrogen, and phosphorus. We analyze and summarize the effects of benthic bioturbation on the environmental behavior of typical pollutants and typical biogeochemical cycling processes near the sediment-water interface, which contributes to an in-depth understanding of the benthic bioturbator-mediated environmental and biogeochemical processes, and to the recognition of the ecological significance of bioturbation.